Socio-Structural and Neighborhood Predictors of Incident Criminal Justice Involvement in a Population-Based Cohort of Yo
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Socio-Structural and Neighborhood Predictors of Incident Criminal Justice Involvement in a Population-Based Cohort of Young Black MSM and Transgender Women Anna L. Hotton & Yen-Tyng Chen & Phil Schumm & Aditya S. Khanna & Russell Brewer & Britt Skaathun & Rodal S. Issema & Santhoshini Ramani & Arthi Ramachandran & Jonathan Ozik & Kayo Fujimoto & Nina T. Harawa & John A. Schneider
# The New York Academy of Medicine 2020
Abstract Black men who have sex with men (MSM) and transgender women are disproportionately affected by criminal justice involvement (CJI) and HIV. This study recruited 618 young Black MSM and transgender A. L. Hotton (*) : Y. 62 indicating presence of clinical symptoms as in previous work [50]. Neighborhood Factors Neighborhood characteristics were derived from multiple administrative sources and past projects, including the 2014 American Community Survey (5-year estimates for 2010–2014 from the US Census Bureau), the Chicago Data Portal, Substance Abuse and Mental Health Services Administration (SAMHSA), Center for Spatial Data Science at the University of Chicago, and a previous study by the authors on affiliation networks of YBMSM [51]. The creation of neighborhood-level variables has been described in detail elsewhere [52]. All neighborhood variables were aggregated to the Chicago
A. Hotton et al.
community area level. The Chicago community areas are clearly delineated and stable and have been used in previous geographic analyses [52]. They are also widely used by government agencies and community stakeholders and are thus a relevant unit of analysis for consideration of potential structural interventions. Participants provided residential addresses at the baseline survey, and addresses were geocoded. Valid geolocation information at the community area level was obtained from 575 of 618 participants. Sociodemographics Sociodemographic variables included age, gender identity and sexual orientation, highest level of educational attainment (current student, less than high school, high school or GED equivalent, or post-secondary education), and annual income ( 1, which together explained 78% of the variance among the set of neighborhood variables. The 3 components were classified as the following: (1) socioeconomic disadvantage (e.g., violent crime, poverty, vacant building density), Black race also loaded on this component; (2) immigrant population (e.g., majority Latino, foreign-born); and (3) instability (e.g., renteroccupied housing, residential instability, high liquor outlet density). An initial set of variables was chosen for evaluation based on a priori hypotheses about potential predictors of CJI. At the individual level, we hypothesized that financial hardship, substance use, and psychological distress would be associated with increased risk of CJI and that resilience factors (e.g., social support, spirituality) would be protective. We also hypothesized that neighborhood characteristics (e.g., concentration of poverty, violent crime) would be associated with increased risk of CJI. Una
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